Motion detection using wireless local area networks

ABSTRACT

This document describes techniques and devices to detect and classify human and animal presence and/or motion based on changes in the interaction of the human or animal with Wireless Local Area Network (WLAN) radio frequency (RF) signals within or about a building structure, such as a home or office. A first WLAN device transmits a sounding packet to a second WLAN device and receives an acknowledgement (ACK) of receiving the sounding packet by the second WLAN device. The first WLAN device uses the received ACK to determine Channel State Information (CSI) for an RF signal path between the first WLAN device and the second WLAN device, aggregates the determined CSI with additional CSI, and uses the aggregated CSI to determine a presence or a motion within the structure.

RELATED APPLICATION(S)

This application is a national stage entry of International ApplicationNo. PCT/US2019/012793, filed Jan. 8, 2019, the disclosure which isincorporated herein by reference in its entirety.

Background

Motion detection of human and animal (pet) activity has been widely usedin applications such as home security systems and home-health-and-safetymonitoring systems. Conventional motion detection systems use media ofvisible light, infrared light, or mechanical vibration, to monitor andrecognize human (and animal) behavior. These conventional techniques allrequire costly installation of new detection devices such as cameras,infrared sensors, or vibration detectors.

Conventional monitoring devices and systems are, to some degree,intrusive on family lives or business activities. These conventionaltechniques may require a user to manage operational configurations toallay privacy concerns that may arise with conventional monitoringdevices.

SUMMARY

This summary is provided to introduce simplified concepts of motiondetection using wireless local area networks. The simplified conceptsare further described below in the Detailed Description. This summary isnot intended to identify essential features of the claimed subjectmatter, nor is it intended for use in determining the scope of theclaimed subject matter.

In aspects, methods, devices, systems, and means for motion detectionusing wireless local area networks (WLANs) are described in which afirst WLAN device transmits a sounding packet to a second WLAN deviceand receives an acknowledgement (ACK) of receiving the sounding packetby the second WLAN device. The first WLAN device uses the received ACKto determine Channel State Information (CSI) for an RF signal pathbetween the first WLAN device and the second WLAN device, aggregates thedetermined CSI with additional CSI, and uses the aggregated CSI todetermine a presence or a motion within a building structure.

In other aspects, methods, devices, systems, and means for motiondetection using wireless local area networks are described in which afirst WLAN device determines that a second WLAN device can be updated totransmit sounding packets for motion classification and initiates anupdate request that is effective to cause a cloud-based server to updatesoftware in the second WLAN device. The first WLAN device receives afirst sounding packet from the second WLAN device, after the update ofthe software of the second WLAN device. The first WLAN device transmitsa first acknowledgement (ACK) of receiving the first sounding packet tothe second WLAN device. The transmission of the first ACK causes thesecond WLAN device to determine Channel State Information (CSI) for aradio frequency (RF) signal path between the first WLAN device and thesecond WLAN device and to transmit the determined CSI to the first WLANdevice.

A method for classifying motion, by a first wireless local area network(WLAN) device is provided. The method comprises transmitting, by thefirst WLAN device, a sounding packet to a second WLAN device andreceiving, from the second WLAN device, an acknowledgement (ACK) ofreceiving the sounding packet by the second WLAN device. The methodfurther comprises using the received ACK, determining Channel StateInformation (CSI) for a radio frequency (RF) signal path between thefirst WLAN device and the second WLAN device, aggregating the determinedCSI with additional CSI, and determining, using the aggregated CSI, apresence or a motion within a structure.

The method may further comprise receiving at least some of theadditional CSI from a third WLAN device.

The first WLAN device may be a mesh WLAN Access Point (AP), the firstWLAN device may be a root AP in a mesh WLAN, the second WLAN device andthe third WLAN device may be APs in the mesh WLAN, and the method mayfurther comprise receiving at least some of the additional CSI from athird WLAN device.

The method may further comprise scheduling, by the first WLAN device,transmission of sounding packets by the first, second, and third WLANdevices and receiving, by the first WLAN device, at least some of theadditional CSI from the second and third WLAN devices.

The sounding packets may be transmitted using a backhaul radio linkbetween the APs in the mesh WLAN.

The first WLAN device may be a WLAN Station (STA), the second WLANdevice and the third WLAN device may be STAs in the WLAN, the method mayfurther comprise scheduling, by the first WLAN device, transmission ofsounding packets by the first, second, and third WLAN devices. Themethod may further comprise receiving, by the first WLAN device, atleast some of the additional CSI from the second and third WLAN devices.

The method may further comprise forwarding the determined presence ormotion to a cloud-based service that is effective to cause thecloud-based service to change state information related to thestructure.

The method of determining the presence or the motion may furthercomprise extracting motion features from the aggregated CSI, selectingmotion features from the extracted motion features, and classifying theselected motion features to determine the presence or the motion.

The determined presence or motion may be one of: no presence, a humanpresence, an animal presence, a human sitting, a human walking, or ahuman falling.

The WLAN may be an IEEE 802.11 WLAN, the sounding packet may be an IEEE802.11 Null Data Frame (NDF), and the ACK may be IEEE 802.11 ACK.

A wireless local area network (WLAN) device is also provided. The WLANdevice comprises a WLAN radio transceiver and a processor and memorysystem to implement a motion sensing manager application. The motionsensing application is configured to transmit, using the WLAN radiotransceiver, a sounding packet to another WLAN device and receive, fromthe other WLAN device, an acknowledgement (ACK) of reception of thesounding packet by the other WLAN device. The motion sensing applicationis further configured to, using the received ACK, determine ChannelState Information (CSI) for a radio frequency (RF) signal path betweenthe WLAN device and the other WLAN device, aggregate the determined CSIwith additional CSI, and determine, using the aggregated CSI, a presenceor a motion within a structure.

The motion sensing manager application may be further configured todetermine the presence or the motion within the structure by: extractingmotion features from the aggregated CSI, selecting motion features fromthe extracted motion features, and classifying the selected motionfeatures to determine the presence or the motion.

The WLAN device may be an IEEE 802.11 station (STA) device or an IEEE802.11 Access Point (AP), and the other WLAN device may be an IEEE802.11 STA device or an IEEE 802.11 AP device.

A method for classifying motion, by a first wireless local area network(WLAN) device in a WLAN network is provided. The method comprisesdetermining, by the first WLAN device, that a second WLAN device can beupdated to transmit sounding packets for motion classification andinitiating an update request that is effective to cause a cloud-basedserver to update software in the second WLAN device. The method furthercomprises receiving, by the first WLAN device, a first sounding packetfrom the second WLAN device after the update of the software of thesecond WLAN device. The method further comprises transmitting, to thesecond WLAN device, a first acknowledgement (ACK) of receiving the firstsounding packet, the transmitting being effective to cause the secondWLAN device to: determine Channel State Information (CSI) for a radiofrequency (RF) signal path between the first WLAN device and the secondWLAN device and transmit the determined CSI to the first WLAN device.

The method may further comprise transmitting, by the first WLAN device,a second sounding packet to the second WLAN device and receiving, fromthe second WLAN device, a second ACK of the second sounding packet beingreceived by the second WLAN device. The method may further compriseusing the received second ACK, determining Channel State Information(CSI) for a radio frequency (RF) signal path between the first WLANdevice and the second WLAN device and receiving additional CSI from thesecond WLAN device. The method may further comprise aggregating thedetermined CSI with the additional CSI and determining, using theaggregated CSI, a presence or a motion within a structure.

The method of determining that the second WLAN device can be updated totransmit sounding packets for motion classification may further comprisesending, by the first WLAN device, a request to a cloud-based service toidentify WLAN devices installed at a structure that can be updated tosupport motion classification.

The initiating the update request may be effective to cause thecloud-based service to send a notification to a user to approve theupdate.

The method may further comprise transmitting additional sounding packetsto a third WLAN device that is not capable of supporting motionclassification. The method may further comprise receiving, from thethird WLAN device, ACKs for the additional sounding packets received bythe third WLAN device, and using the ACKs for the additional soundingpackets, determining CSI for an RF signal path between the first WLANdevice and the third WLAN device.

The first WLAN device may be an IEEE 802.11 station (STA) device, thesecond WLAN device may be an IEEE 802.11 STA device, and the third WLANdevice may be an IEEE 802.11 Access Point (AP).

The method may further comprise scheduling, by the first WLAN device,transmissions of sounding packets by the second WLAN device. The methodmay further comprise transmitting the schedule for sounding packettransmissions to the second WLAN device, the transmitting causing thesecond WLAN device to transmit sounding packets according to theschedule.

The WLAN may be an IEEE 802.11 WLAN, the sounding packet may be an IEEE802.11 Null Data Frame (NDF), and the ACK may be an IEEE 802.11 ACK.

The details of one or more implementations are set forth in theaccompanying drawings and the following description. Other features andadvantages will be apparent from the description and drawings and fromthe claims. This summary is provided to introduce subject matter that isfurther described in the Detailed Description and Drawings. Accordingly,this summary should not be considered to describe essential features norused to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of motion detection using wireless local area networks aredescribed with reference to the following drawings. The same numbers areused throughout the drawings to reference like features and components:

FIG. 1 illustrates an example environment that illustrates thedisturbance of WLAN RF signals by human motion within a structure.

FIG. 2 illustrates an example system in which various aspects andtechniques of motion detection using wireless local area networks can beimplemented.

FIG. 3 illustrates an example system in which various aspects andtechniques of motion detection using wireless local area networks can beimplemented, as generally related to motion sensing by a mesh WLANdeployed in a structure.

FIG. 4 illustrates an example system in which various aspects andtechniques of motion detection using wireless local area networks can beimplemented.

FIG. 5 illustrates an example system in which various aspects andtechniques of motion detection using wireless local area networks can beimplemented, as generally related to motion sensing by a mesh WLAN andWLAN client devices deployed in a structure.

FIG. 6 illustrates an example method of motion detection using wirelesslocal area networks as in accordance with aspects of the techniquesdescribed herein.

FIG. 7 illustrates an example method of motion detection using wirelesslocal area networks in accordance with aspects of the techniquesdescribed herein.

FIG. 8 illustrates an example environment in which a WLAN can beimplemented in accordance with aspects of the techniques describedherein.

FIG. 9 illustrates an example WLAN device that can be implemented in aWLAN environment in accordance with one or more aspects of thetechniques described herein.

FIG. 10 illustrates an example system with an example device that canimplement aspects of motion detection using wireless local areanetworks.

DETAILED DESCRIPTION

This document describes techniques and devices to detect and classifyhuman and animal presence and/or motion based on changes in theinteraction of the human or animal with Wireless Local Area Network(WLAN) radio frequency (RF) signals within or about a buildingstructure, such as a home or office. A first WLAN device transmits asounding packet to a second WLAN device and receives an acknowledgement(ACK) of receiving the sounding packet by the second WLAN device. Thefirst WLAN device uses the received ACK to determine Channel StateInformation (CSI) for an RF signal path between the first WLAN deviceand the second WLAN device, aggregates the determined CSI withadditional CSI, and uses the aggregated CSI to determine a presence or amotion within the structure.

As the Internet-of-Things (IoT) expands, smart devices continue toproliferate. Internet-connected thermostats, appliances, vehicles,phones, lights, and machines are found in all areas of life, includinghome, work, business, recreation, and school. Many of theseInternet-connected devices communicate using wireless networks andwireless protocols. Although a primary purpose of such wirelesscommunication is to transfer information between or among the variousdevices, the transmission signals themselves can provide additionalinformation. In particular, changes or variations in the transmissionsignals can indicate presence or lack of presence of a person, animal,or object in the physical space of the wireless network. Changes orvariations can also indicate motion, lack of motion, changes in motion,or cessation of motion in the physical space of the wireless network.Advantageously, the additional information can be derived from theexisting wireless network without additional equipment, hardware, orother devices.

Wireless networks, including mesh networks, can not only transmitinformation among various network devices but can also monitor changesor variations in the transmission signals used to communicate theinformation. Monitoring changes or variations in transmission signalscan indicate presence, motion, lack of motion, or other characteristicsabout the physical space in which the wireless network operates withoutthe need for additional equipment or dedicated devices.

Wireless networks, such as Wi-Fi, Bluetooth, and mesh networks likeZigbee, Z-Wave, and others all involve wirelessly connected components.Some components include routers, switches, plugs, repeaters, lights,thermostats, appliances, and many other types of devices. The wirelesscomponents can also connect to and interact with one another. Forexample, a home theater system that is turning on could send a signalthat instructs the lights in the room to dim after a certain amount oftime. The components may also be connected to other devices via theinternet or other network connections. In such cases, the wirelesscomponents can be accessed and manipulated using a web browser, anapplication on a mobile device, a local remote, or programing system(e.g., voice commands, home automation systems).

As these various devices communicate with one another, RF signals passthroughout the physical space or geography of the wireless network. Asdiscussed below, when a person moves through the physical space, thepropagation of the RF signals is affected by the presence of the person.Machine Learning (ML) techniques can be used to train an ML system toclassify human activities based on the changes in the propagation of theRF signals.

Conventional ML techniques for this classification are based on imageclassification techniques and require relatively large amounts of inputdata and computing resources to perform the classifications. In aspects,motion detection using wireless local area networks performs featureextraction on Channel State Information (CSI) data to detect andclassify presence and motion. The use of feature extraction from CSIdata reduces the volume of data required as an input to classification,as well as reducing the computation resources required for presence andmotion detection.

In other aspects, CSI data can be acquired using various WLAN-enableddevices, such as Access Points (APs) and/or WLAN client devices (e.g.,WLAN Station (STA) devices). The WLAN-enabled devices can operate in aWLAN network using any suitable network topology such as a bus network,a star network, a ring network, a mesh network, a star-bus network, atree, or hierarchical network, and the like.

For example, multiple WLAN (e.g., IEEE 802.11, Wi-Fi) APs providing amesh WLAN network can acquire the CSI data for transmissions between theWLAN APs. In another example, WLAN client devices being served by asingle WLAN AP can acquire the CSI data based on transmissions betweenthe WLAN client devices and/or transmissions between the WLAN clientdevices and the single WLAN AP.

In another aspect, a device local to the WLAN can process the CSI datato detect motion, or a cloud service can process the CSI data to detectmotion. For example, a root AP in a mesh LAN may receive CSI data fromother APs and perform the motion detection. In an alternative example,an AP or STA device can forward the CSI data to a cloud service wherethe motion detection is performed.

In a further aspect, the collection of CSI data is based onstandards-defined communication techniques. For example, a first WLANdevice can transmit a null data frame (NDF) to a second device. Thesecond device responds with an acknowledgement (ACK) of the null dataframe. The first wireless device calculates the CSI data based on thereceived acknowledgement. By using standards-defined communication forchannel probing, any WLAN device can be updated (e.g., downloading anupdated software image to the device) to add motion detectioncapabilities in upper layer(s) of the network stack of the WLAN device,without modification of the standards-based Media Access Control (MAC)and Physical (PHY) layers of the WLAN network stack and the WLAN radiohardware.

Example Environment

FIG. 1 illustrates an example environment 100 that illustrates thedisturbance of WLAN RF signals by human motion within a structure. Anumber of RF signal paths are illustrated between a first WLAN device102 and a second WLAN device 104. An RF signal 106 can propagatedirectly (e.g., line-of-sight (LOS) propagation) from the first WLANdevice 102 to the second WLAN device 104. Other RF signals between thefirst WLAN device 102 and the second WLAN device 104 are reflectedsignals. For example, an RF signal 108 is reflected off a wall 110 of astructure where the WLAN is deployed. The characteristics of the RFsignals 106 and 108 received at the second WLAN device 104 will remainconstant over time as long as the physical relationship of the firstWLAN device 102, the second WLAN device 104 and the surroundingstructure are unchanged.

Other RF signals between the first WLAN device 102 and the second WLANdevice 104 can vary based on the motion of humans (or animals) withinthe structure. For example, an RF signal 112 reflects off a person atposition 114 instead of propagating until the RF signal 112 reaches anelement of the structure. The alteration of the reflection, as comparedto reflections in an empty structure, can be used to detect the presenceof the person at position 114. As the person moves to the position 116,an RF signal 118 is reflected off the person. Changes in thecharacteristics of multiple RF signals over time can be used todetermine motion, whether a human or an animal, such as a dog, is inmotion and/or moving, and what type of motion is detected, such aswalking, sitting, falling, or the like.

In addition to changing characteristics of reflected RF signals, LOSsignals can also be affected (e.g., attenuated) by the presence of thehuman. For example, if the human continued along the path of motion to apoint that intersects the RF signal 106 between the first WLAN device102 and the second WLAN device 104 (not illustrated in FIG. 1 for thesake of clarity), the RF signal 106 would be attenuated due to blockingof the RF signal 106 by the human.

Presence and Motion Detection

To acquire WLAN channel information, WLAN devices (initiators) arescheduled to periodically transmit sounding packets, such as unicastIEEE 802.11 Null Data Frames (NDFs), to another WLAN device. The WLANdevice that receives the NDF responds to the initiator by transmittingan acknowledgement (ACK), such as an IEEE 802.11 ACK, to the initiator.The ACK is used as a sounding signal for the RF channel(s) between theinitiator and the other WLAN device.

For example, in a mesh WLAN system with multiple APs, a root AP mayschedule periodic NDF transmissions to other APs in the mesh network toacquire channel soundings between the root AP and each of the other APs.The APs are typically stationary devices which provide soundings betweenfixed locations in the structure where the WLAN is deployed. The root APmay also schedule other APs to transmit NDFs to acquire soundinginformation for the locations of the other APs. Alternatively oroptionally, the APs receiving an NDF can use the received NDF as asounding signal. In another alternative or option, other WLAN STAdevices that are stationary (e.g., a WLAN-connected thermostat, atelevision streaming device, a WLAN-connected camera, or the like) canbe included as initiators and/or recipients of NDFs for acquiringchannel sounding data. Other example WLAN configurations and soundingsin those configurations is described in further detail below.

The initiator(s) calculate a radio link signal spectrogram and channelstate information (CSI) based on the ACK packet. Alternatively oroptionally, the APs receiving an NDF can calculate a radio link signalspectrogram and CSI based on the received NDF. In a further alternativeor option, non-null data frames received by a WLAN device can also beutilized to extract radio link signal spectrograms and CSI.

The radio link signal spectrograms and channel state information (CSI)acquired by WLAN devices in the WLAN are aggregated for recognition ofpresence, motion, and/or human behavior at a detection server. Both theaggregated radio signal spectrogram and CSI data from the WLAN links arefed as inputs to a Machine-Learning (ML) algorithm, executed at thedetection server, to recognize human behavior in the proximity of WLANdevices. The detection server can be located on a device in the WLAN,such as the root AP of the WLAN, at a remote (cloud-based) serverdevice, or both. Alternatively or optionally, portions of therecognition processing can be distributed in any suitable manner amongdevices in the WLAN and or in the cloud.

FIG. 2 illustrates an example system 200 in which various aspects andtechniques of motion detection using wireless local area networks can beimplemented. The system 200 includes WLAN devices 202 and the detectionserver 204 to illustrate processing radio signal spectrograms andchannel state information (CSI) to detect and classify presence andmotion behaviors.

As described above, the detection server 204 aggregates the radio signalspectrograms and CSI from the WLAN devices 202. Optionally oradditionally, the radio signal spectrograms and CSI data may bepreprocessed before feature extraction. For example, the preprocessingmay include noise reduction, dimension reduction, or the like. In orderfor an ML model to use the channel-state-information (CSI) to classifyamong different motion classes, such as no-motion, human motion, and petmotion, an effective and efficient feature vector must be extracted froma sequence of CSI samples over a time interval. The required CSI featurevector must capture the granular pattern differences introduced ontoCSIs by different motion classes. Consequently, the feature vectorserves as the input to the downstream ML model which learns thedifferent CSI patterns captured through the feature vector on differentmotion classes.

A motion feature extractor 206 performs a bi-projection featureextraction. The motion feature extractor 206 buffers the CSI on atwo-dimensional plane, where the x-axis of the plane is a CSI sampleindex (time), and the y-axis of the plane is a subcarrier index (RFfrequency). By way of example and not limitation, each CSI sample CSI(t)that is buffered includes 424 subcarriers. Each subcarrier isrepresented as a complex number which is a channel-response-coefficientof the RF channel for the particular subcarrier between a WLANtransmitter in a first WLAN device and a WLAN receiver in a second WLANdevice. By way of example and not limitation, a buffer interval of 5 secin time with a 20 Hz CSI sampling rate is used which results in a CSIbuffer block of 424 subcarriers by 100 CSI samples in the frequency-timeplane.

Conventional techniques that process CSI data using image-relatedfeature extraction methods require larger input sets of CSI data andgreater processing resources than techniques using statistics of CSImotion patterns, such as variance of CSI block in temporal and frequencydomain, that directly correlate to the physical motion of human orobject in space-time that introduce disturbances onto CSI. Thecorrelation between the CSI temporal-frequency variance and the physicalmotion disturbing RF channel in space-time performed by the motionfeature extractor 206 results in classifications using less data andfewer processing resources.

Training data 208 is provided to the detection server 204 to train themotion feature selector 210 and the motion machine learning (ML)classifier 212 to identify motion or presence that corresponds to one ofthe classifications 214, such as no presence, human presence, animalpresence, human sitting, human walking, human falling, or the like. Forexample, the feature vectors extracted from the sequence of CSI samplesare processed through two fully connected layers to identify motion orpresence that corresponds to one of the classifications 214.

Sensing Systems

FIG. 3 illustrates an example system 300 in which various aspects andtechniques of motion detection using wireless local area networks can beimplemented, as generally related to motion sensing by a mesh WLANdeployed in a structure 302. The mesh WLAN includes a root mesh accesspoint 304, a mesh access point 306, and a mesh access point 308. Theroot mesh AP 304 connects the mesh WLAN to a communication network 310(e.g., the Internet) and, in turn, to one or more cloud services 312,such as a cloud-based detection server 204, a smart-home cloud service,or the like. Although the mesh WLAN is illustrated with three mesh APs,any suitable number of mesh APs may be included in the mesh WLAN.

The root mesh AP 304 and the mesh APs 306 and 308 are interconnected bybackhaul (or backbone) links 314, 316, and 318 that carry networktraffic for client devices served by the APs to other client devices inthe mesh WLAN or to nodes or services connected to the communicationnetwork 310. For example, the root mesh AP 304 can configure backhaulcommunications with and between the mesh APs 306 and 308 on a particularWLAN channel or in a particular WLAN RF frequency band (e.g., a 5 GHzradio band or a 2.4 GHz radio band). The root mesh AP 304 and the meshAPs 306 and 308 provide WLAN connectivity to WLAN client devices in thesame radio band as the backhaul communications links, a different radioband than the backhaul communications links, or both.

The root mesh AP 304 schedules the periodic transmission of NDFs by theroot mesh AP 304 and the mesh APs 306 and 308 across the backhaul links314, 316, and 318 to acquire radio signal spectrogram and CSI data formotion classification. The root mesh AP 304 receives the acquired radiosignal spectrogram and CSI data from the mesh APs 306 and 308. In oneaspect, the root mesh AP 304 includes the detection server 204,aggregates the acquired radio signal spectrogram and CSI data, andperforms the motion classification. The root mesh AP 304 sendsclassification result(s) to applications and services that consume theclassification data, such as a smart-home cloud service, a securitymonitoring service, a security system hub in the structure 302, or thelike.

In another aspect, the root mesh AP 304 aggregates the acquired radiosignal spectrogram and CSI data and forwards the aggregated data to acloud-based detection server 204. The cloud-based detection server 204may be a service that performs the motion classification and providesthe classification results to other applications, services, and/ordevices, or the detection server 204 may be included as a component inanother cloud-based service, such as a smart-home cloud service, asecurity monitoring service, or the like.

FIG. 4 illustrates an example system 400 in which various aspects andtechniques of motion detection using wireless local area networks can beimplemented, as generally related to motion sensing in a WLAN served byan access point without capabilities to configure NDF transmissions toacquire radio signal spectrograms and CSI and/or classify presence ormotion from the acquired data. The WLAN is deployed in a structure 402and includes a WLAN access point 404 that connects the WLAN to thecommunication network 310 and in turn to one or more cloud services 312,such as a cloud-based detection server 204, a smart-home cloud service,or the like.

The WLAN includes multiple WLAN client devices 406, 408, and 410 thatare connected to WLAN by the WLAN AP 404. WLAN client devices may bemobile or nomadic, such as a laptop computer or a smartphone, orstationary, such as a media streaming device, a camera, a thermostat, asmart-speaker, or the like. Any WLAN client device can include thecapability to schedule NDF transmissions for channel sounding, toaggregate acquired channel data, and/or to perform motionclassification. For example, the WLAN client device 406 is a stationarydevice, such as a media streaming device attached to a television. TheWLAN client device 406 determines that WLAN client devices 408 and 410are also stationary devices. The WLAN client device 406 may make thisdetermination in any suitable manner, such as transmitting NDFs over aperiod of time and determining that the devices are stationary, bydetermining a device type or identifier that indicates the devices arestationary, by querying a cloud service, such as a smart-home service,that can identify devices installed at the structure 402, or the like.The WLAN client device 406 schedules NDF transmissions to otherstationary WLAN client devices, such as WLAN client devices 408 and 410to acquire radio signal spectrograms and CSI.

Continuing with the example, the WLAN client device 406 may alsodetermine that other WLAN client devices, such as WLAN client devices408 and 410, can schedule NDF transmissions and acquire radio signalspectrograms and CSI, as illustrated at 412, 414, and 416. The WLANclient device 406 schedules NDF transmissions for the other client WLANdevice and receives the radio signal spectrograms and CSI from the otherWLAN client devices.

In one aspect, the WLAN client device 406 includes the detection server204, aggregates the acquired radio signal spectrogram and CSI data, andperforms the motion classification. The WLAN client device 406 sends toclassification result(s) to applications and services that consume theclassification data, such as a smart-home cloud service, a securitymonitoring service, a security system hub in the structure 402, or thelike.

In another aspect, the WLAN client device 406 aggregates the acquiredradio signal spectrogram and CSI data and forwards the aggregated datato a cloud-based detection server 204. The cloud-based detection server204 may be a service that performs the motion classification andprovides the classification results to other applications, services,and/or devices, or the detection server 204 may be included as acomponent in another cloud-based service, such as a smart-home cloudservice, a security monitoring service, or the like.

FIG. 5 illustrates an example system 500 in which various aspects andtechniques of motion detection using wireless local area networks can beimplemented, as generally related to motion sensing by a mesh WLAN andWLAN client devices deployed in a structure 502. The mesh WLAN includesa root mesh access point 504, a mesh access point 506, and a mesh accesspoint 508. The root mesh AP 504 connects the mesh WLAN to thecommunication network 310 and in turn to one or more cloud services 312,such as a cloud-based detection server 204, a smart-home cloud service,or the like. Although the mesh WLAN is illustrated with three mesh APs,any suitable number of mesh APs may be included in the mesh WLAN.

The root mesh AP 504 and the mesh APs 506 and 508 may operate usingbackhaul links 510, 512, and 514 (as illustrated by the dashed lines inFIG. 5 ) to acquire radio signal spectrogram and CSI data, as describedwith respect to FIG. 3 , above. However, as illustrated in FIG. 5 , thelocation of the root mesh AP 504 and the mesh APs 506 and 508 mayprovide limited coverage of the structure 502 either due to theplacement of the mesh APs, the location, number, and composition ofwalls, floors, and ceilings in the structure 502, link budgetlimitations due to noise and interference, or the like.

In one aspect, the root mesh AP 504 schedules NDF transmissions toclient WLAN devices 516, 518, 520 from the mesh WLAN APs to gatheradditional radio signal spectrograms and CSI for additional RF signalpaths within the structure 502, as shown by the dash-dot lines at 522,524, 526, and 528. Additionally or optionally, the root mesh AP 504determines that one or more of the WLAN client devices, in this examplethe WLAN client device 518, has the capability to perform scheduled NDFtransmissions and acquire radio signal spectrograms and CSI. The rootmesh AP 504 configures the WLAN client device 518 to periodicallytransmit NDFs to the client devices 516 and 520, as shown by the dottedlines 530 and 532, and acquires radio signal spectrograms and CSI.

By including additional RF signal paths between the additional WLANdevices and/or the APs, additional radio signal spectrogram and CSI datais available to improve coverage and accuracy of feature extraction andclassification for presence and motion detection. The root mesh AP 504can schedule the NDF transmissions in a suitable radio band, channel, orcombination of radio bands and channels between the mesh WLAN APs andWLAN client devices. The root mesh AP 504 can schedule the NDFtransmissions based on the capabilities of the mesh WLAN APs and WLANclient devices, RF signal propagation characteristics in the structure502, to improve link budgets for NDF transmissions, or the like.

In a further aspect, in system 400 or 500, there may be WLAN clientdevices that are capable (e.g., devices that include compatible hardwareand a compatible networking stack) of performing scheduled NDFtransmissions and acquiring radio signal spectrograms and CSI but lackhigher layer software to perform these operations. These WLAN clientdevices can be updated with the capabilities of performing scheduled NDFtransmissions and acquiring radio signal spectrograms and CSI byupgrading the software of the device, such as by downloading an updatedsoftware image to the device.

The decision to update WLAN client devices for motion and presencedetection may be based on a variety of factors. For example, thedetection server 204 may determine that the success rate forclassification is below a threshold. Based on the determination, thedetection server 204 may provide a user notification (e.g., via anemail, text message, in-app notification, or the like) to the user thatindicates that the classification accuracy can be improved by addingadditional, motion-detection-capable WLAN devices. An application, sucha smart-home application on a user device, can be triggered to searchfor capable devices to upgrade, to trigger the root mesh WLAN AP tosearch for capable devices to upgrade, or to query a cloud-service, suchas a smart-home cloud service that maintains a list of WLAN clientdevices installed in the structure. Based on receiving the query, thecloud-service determines which WLAN client devices can be upgraded andprovides a notification to the user so that the user can initiate theupgrades or, alternatively, the cloud-based service can push thesoftware upgrade to the target devices without further userintervention.

In an aspect, updating WLAN client devices for motion and presencedetection may be triggered by adding a device to the WLAN. For example,adding a security hub device to a smart-home system in the structure maytrigger a smart-home cloud service connected to the smart-home system todetermine to push updated software images to other devices in thesmart-home system to enable or improve motion and presence detection foruse by the security functions in the security hub device and smart-homesystem.

In another aspect, adding a monitoring service, such as asecurity-monitoring service, to a smart-home system can trigger updatingWLAN client devices for motion and presence detection. For example,adding the security-monitoring service to a smart-home system in thestructure may trigger a smart-home cloud service, connected to thesmart-home system, to determine to push updated software images to otherdevices in the smart-home system to enable or improve motion andpresence detection for use by the security-monitoring service that usesthe smart-home system.

Example Methods

Example methods 600 and 700 are described with reference to FIGS. 6 and7 in accordance with one or more aspects of motion detection usingwireless local area networks. The order in which the method blocks aredescribed is not intended to be construed as a limitation, and anynumber of the described method blocks can be combined in any order orskipped to implement a method or an alternate method. Generally, any ofthe components, modules, methods, and operations described herein can beimplemented using software, firmware, hardware (e.g., fixed logiccircuitry), manual processing, or any combination thereof. Someoperations of the example methods may be described in the generalcontext of executable instructions stored on computer-readable storagememory that is local and/or remote to a computer processing system, andimplementations can include software applications, programs, functions,and the like. Alternatively or in addition, any of the functionalitydescribed herein can be performed, at least in part, by one or morehardware logic components, such as, and without limitation,Field-programmable Gate Arrays (FPGAs), Application-specific IntegratedCircuits (ASICs), Application-specific Standard Products (ASSPs),System-on-a-chip systems (SoCs), Complex Programmable Logic Devices(CPLDs), and the like.

FIG. 6 illustrates example method(s) 600 of motion detection usingwireless local area networks as generally related to a WLAN device in aWLAN network. At block 602, a first WLAN device (e.g., WLAN device 202)transmits a sounding packet to a second WLAN device (e.g., WLAN device202).

At block 604, the first WLAN device receives, from the second WLANdevice, an acknowledgement (ACK) of receiving the sounding packet by thesecond WLAN device.

At block 606, the first WLAN device uses the received ACK to determineChannel State Information (CSI) for a radio frequency (RF) signal pathbetween the first WLAN device and the second WLAN device.

At block 608, the first WLAN device aggregates the determined CSI withadditional CSI.

At block 610, the first WLAN device uses the aggregated CSI to determinea presence or a motion within a structure.

FIG. 7 illustrates example method(s) 700 of motion detection usingwireless local area networks as generally related to a WLAN device in aWLAN network. At block 702, the WLAN device (e.g., WLAN device 202)determines that a second WLAN device can be updated to transmit soundingpackets for motion classification.

At block 704, the first WLAN device initiates an update request that iseffective to cause a cloud-based server (e.g., the cloud service 312) toupdate software in the second WLAN device.

At block 706, the first WLAN device receives a first sounding packetfrom the second WLAN device after the update of the software of thesecond WLAN device.

At block 708, the first WLAN device transmits a first acknowledgement(ACK) of receiving the first sounding packet to the second WLAN device,the transmitting being effective to cause the second WLAN device todetermine Channel State Information (CSI) for a radio frequency (RF)signal path between the first WLAN device.

Example Environments and Devices

FIG. 8 illustrates an example environment 800 in which aspects of motiondetection using wireless local area networks can be implemented.Generally, the environment 800 includes a WLAN 801, implemented as partof a smart-home or other type of structure with any number of WLANdevices 202 that are configured for communication in a WLAN. Forexample, the WLAN devices can include a thermostat 802, hazard detectors804 (e.g., for smoke and/or carbon monoxide), cameras 806 (e.g., indoorand outdoor), lighting units 808 (e.g., indoor and outdoor), and anyother types of WLAN devices 810 that are implemented inside and/oroutside of a structure 812 (e.g., in a smart-home environment). In thisexample, the WLAN devices can also include any of the previouslydescribed devices.

In the environment 800, any number of the WLAN devices 810 can beimplemented for wireless interconnection to wirelessly communicate andinteract with each other. The WLAN devices 810 are modular, intelligent,multi-sensing, network-connected devices that can integrate seamlesslywith each other and/or with a central server or a cloud-computing systemto provide any of a variety of useful smart-home objectives andimplementations. An example of a WLAN device 810 that can be implementedas any of the devices described herein is shown and described withreference to FIG. 9 .

In implementations, the thermostat 802 may include a Nest® LearningThermostat that detects ambient climate characteristics (e.g.,temperature and/or humidity) and controls a HVAC system 814 in thesmart-home environment. The learning thermostat 802 and other smartdevices “learn” by capturing occupant settings to the devices. Forexample, the thermostat learns preferred temperature set-points formornings and evenings, and when the occupants of the structure areasleep or awake, as well as when the occupants are typically away or athome.

A hazard detector 804 can be implemented to detect the presence of ahazardous substance or a substance indicative of a hazardous substance(e.g., smoke, fire, or carbon monoxide). In examples of wirelessinterconnection, a hazard detector 804 may detect the presence of smoke,indicating a fire in the structure, in which case the hazard detectorthat first detects the smoke can broadcast a low-power wake-up signal toall of the connected WLAN devices. The other hazard detectors 804 canthen receive the broadcast wake-up signal and initiate a high-powerstate for hazard detection and to receive wireless communications ofalert messages. Further, the lighting units 808 can receive thebroadcast wake-up signal and activate in the region of the detectedhazard to illuminate and identify the problem area. In another example,the lighting units 808 may activate in one illumination color toindicate a problem area or region in the structure, such as for adetected fire or break-in, and activate in a different illuminationcolor to indicate safe regions and/or escape routes out of thestructure.

In various configurations, the WLAN devices 810 can include an entrywayinterface device 816 that functions in coordination with anetwork-connected door lock system 818, and that detects and responds toa person's approach to or departure from a location, such as an outerdoor of the structure 812. The entryway interface device 816 caninteract with the other WLAN devices based on whether someone hasapproached or entered the smart-home environment. An entryway interfacedevice 816 can control doorbell functionality, announce the approach ordeparture of a person via audio or visual means, and control settings ona security system, such as to activate or deactivate the security systemwhen occupants come and go. The WLAN devices 810 can also include othersensors and detectors, such as to detect ambient lighting conditions,detect room-occupancy states (e.g., with an occupancy sensor 820), andcontrol a power and/or dim state of one or more lights. In someinstances, the sensors and/or detectors may also control a power stateor speed of a fan, such as a ceiling fan 822. Further, the sensorsand/or detectors may detect occupancy in a room or enclosure and controlthe supply of power to electrical outlets or devices 824, such as if aroom or the structure is unoccupied.

The WLAN devices 810 may also include connected appliances and/orcontrolled systems 826, such as refrigerators, stoves and ovens,washers, dryers, air conditioners, pool heaters 828, irrigation systems830, security systems 832, and so forth, as well as other electronic andcomputing devices, such as televisions, entertainment systems,computers, intercom systems, garage-door openers 834, ceiling fans 822,control panels 836, and the like. When plugged in, an appliance, device,or system can announce itself to the WLAN as described above and can beautomatically integrated with the controls and devices of the WLAN, suchas in the smart-home. It should be noted that the WLAN devices 810 mayinclude devices physically located outside of the structure, but withinwireless communication range, such as a device controlling a swimmingpool heater 828 or an irrigation system 830.

As described above, the WLAN includes a WLAN access point (e.g., AP 304,404, or 504) that interfaces for communication with an external network,outside the WLAN. The access point connects to the communication network310, such as the Internet. A cloud service 312, which is connected viathe communication network 310, provides services related to and/or usingthe devices within the WLAN. By way of example, the cloud service 312can include applications for connecting end user devices 838, such assmart phones, tablets, and the like, to devices in the WLAN, processingand presenting data acquired in the WLAN to end users, linking devicesin one or more WLANs to user accounts of the cloud service 312,provisioning and updating devices in the WLAN, and so forth. Forexample, a user can control the thermostat 802 and other WLAN devices inthe smart-home environment using a network-connected computer orportable device, such as a mobile phone or tablet device. Further, theWLAN devices can communicate information to any central server orcloud-computing system via the access point 404. The data communicationscan be carried out using any of a variety of custom or standard wirelessprotocols (e.g., IEEE 802.11, Wi-Fi, ZigBee, Z-Wave, 6LoWPAN, Thread,etc.) and/or by using any of a variety of custom or standard wiredprotocols (CAT6 Ethernet, HomePlug, etc.).

Any of the WLAN devices in the WLAN can serve as low-power andcommunication nodes to create the WLAN in the smart-home environment.Individual low-power nodes of the network can regularly send outmessages regarding what they are sensing, and the other low-powerednodes in the environment—in addition to sending out their ownmessages—can repeat the messages, thereby communicating the messagesfrom node to node (i.e., from device to device) throughout the WLAN. TheWLAN devices can be implemented to conserve power, particularly whenbattery-powered, utilizing low-powered communication protocols toreceive the messages, translate the messages to other communicationprotocols, and send the translated messages to other nodes and/or to acentral server or cloud-computing system. For example, an occupancyand/or ambient light sensor can detect an occupant in a room as well asmeasure the ambient light, and activate the light source when theambient light sensor 840 detects that the room is dark and when theoccupancy sensor 820 detects that someone is in the room. Further, thesensor can include a low-power wireless communication chip (e.g., anIEEE 802.11 chip) that regularly sends out messages regarding theoccupancy of the room and the amount of light in the room, includinginstantaneous messages coincident with the occupancy sensor detectingthe presence of a person in the room. As mentioned above, these messagesmay be sent wirelessly, using the WLAN, from node to node (i.e., smartdevice to smart device) within the smart-home environment as well asover the Internet to a central server or cloud-computing system.

In other configurations, various ones of the WLAN devices can functionas “tripwires” for an alarm system in the smart-home environment. Forexample, in the event a perpetrator circumvents detection by alarmsensors located at windows, doors, and other entry points of thestructure or environment, the alarm could still be triggered byreceiving an occupancy, motion, heat, sound, etc. message from one ormore of the low-powered mesh nodes in the WLAN. In otherimplementations, the WLAN can be used to automatically turn on and offthe lighting units 808 as a person transitions from room to room in thestructure. For example, the WLAN devices can detect the person'smovement through the structure and communicate corresponding messagesvia the nodes of the WLAN. Using the messages that indicate which roomsare occupied, other WLAN devices that receive the messages can activateand/or deactivate accordingly. As referred to above, the WLAN can alsobe utilized to provide exit lighting in the event of an emergency, suchas by turning on the appropriate lighting units 808 that lead to a safeexit. The light units 808 may also be turned-on to indicate thedirection along an exit route that a person should travel to safely exitthe structure.

The various WLAN devices may also be implemented to integrate andcommunicate with wearable computing devices 842, such as may be used toidentify and locate an occupant of the structure, and adjust thetemperature, lighting, sound system, and the like accordingly. In otherimplementations, RFID sensing (e.g., a person having an RFID bracelet,necklace, or key fob), synthetic vision techniques (e.g., video camerasand face recognition processors), audio techniques (e.g., voice, soundpattern, vibration pattern recognition), ultrasound sensing/imagingtechniques, and infrared or near-field communication (NFC) techniques(e.g., a person wearing an infrared or NFC-capable smartphone), alongwith rules-based inference engines or artificial intelligence techniquesthat draw useful conclusions from the sensed information as to thelocation of an occupant in the structure or environment.

In other implementations, personal comfort-area networks, personalhealth-area networks, personal safety-area networks, and/or other suchhuman-facing functionalities of service robots can be enhanced bylogical integration with other WLAN devices and sensors in theenvironment according to rules-based inferencing techniques orartificial intelligence techniques for achieving better performance ofthese functionalities. In an example relating to a personal health-area,the system can detect whether a household pet is moving toward thecurrent location of an occupant (e.g., using any of the WLAN devices andsensors), along with rules-based inferencing and artificial intelligencetechniques. Similarly, a hazard detector service robot can be notifiedthat the temperature and humidity levels are rising in a kitchen, andtemporarily raise a hazard detection threshold, such as a smokedetection threshold, under an inference that any small increases inambient smoke levels will most likely be due to cooking activity and notdue to a genuinely hazardous condition. Any service robot that isconfigured for any type of monitoring, detecting, and/or servicing canbe implemented as a mesh node device on the WLAN, conforming to thewireless interconnection protocols for communicating on the WLAN.

The WLAN devices 810 may also include a smart alarm clock 844 for eachof the individual occupants of the structure in the smart-homeenvironment. For example, an occupant can customize and set an alarmdevice for a wake time, such as for the next day or week. Artificialintelligence can be used to consider occupant responses to the alarmswhen they go off and make inferences about preferred sleep patterns overtime. An individual occupant can then be tracked in the WLAN based on aunique signature of the person, which is determined based on dataobtained from sensors located in the WLAN devices, such as sensors thatinclude ultrasonic sensors, passive IR sensors, and the like. The uniquesignature of an occupant can be based on a combination of patterns ofmovement, voice, height, size, etc., as well as using facial recognitiontechniques.

In an example of wireless interconnection, the wake time for anindividual can be associated with the thermostat 802 to control the HVACsystem in an efficient manner so as to pre-heat or cool the structure todesired sleeping and awake temperature settings. The preferred settingscan be learned over time, such as by capturing the temperatures set inthe thermostat before the person goes to sleep and upon waking up.Collected data may also include biometric indications of a person, suchas breathing patterns, heart rate, movement, etc., from which inferencesare made based on this data in combination with data that indicates whenthe person actually wakes up. Other WLAN devices can use the data toprovide other smart-home objectives, such as adjusting the thermostat802 so as to pre-heat or cool the environment to a desired setting, andturning-on or turning-off the lights 808.

In implementations, the WLAN devices can also be utilized for sound,vibration, and/or motion sensing such as to detect running water anddetermine inferences about water usage in a smart-home environment basedon algorithms and mapping of the water usage and consumption. This canbe used to determine a signature or fingerprint of each water source inthe home, and is also referred to as “audio fingerprinting water usage.”Similarly, the WLAN devices can be utilized to detect the subtle sound,vibration, and/or motion of unwanted pests, such as mice and otherrodents, as well as by termites, cockroaches, and other insects. Thesystem can then notify an occupant of the suspected pests in theenvironment, such as with warning messages to help facilitate earlydetection and prevention.

The environment 800 may include one or more WLAN devices that functionas a hub 846. The hub 846 may be a general-purpose home automation hub,or an application-specific hub, such as a security hub, an energymanagement hub, an HVAC hub, and so forth. The functionality of a hub846 may also be integrated into any WLAN device, such as a smartthermostat device or a smart speaker 848. Hosting functionality on thehub 846 in the structure 812 can improve reliability when the user'sinternet connection is unreliable, can reduce latency of operations thatwould normally have to connect to the cloud service 312, and can satisfysystem and regulatory constraints around local access between WLANdevices.

Additionally, the example environment 800 includes the smart-speaker848. The smart-speaker 848 provides voice assistant services thatinclude providing voice control of smart-home devices. The functions ofthe hub 846 may be hosted in the smart-speaker 848. The smart-speaker848 can be configured to communicate via the WLAN, ZigBee, Z-Wave,Thread, or any combination thereof.

FIG. 9 illustrates an example WLAN device 900 that can be implemented asany of the WLAN devices in a WLAN in accordance with one or more aspectsof motion detection using wireless local area networks as describedherein. The device 900 can be integrated with electronic circuitry,microprocessors, memory, input output (I/O) logic control, communicationinterfaces and components, as well as other hardware, firmware, and/orsoftware to implement the device in a WLAN. Further, the WLAN device 900can be implemented with various components, such as with any number andcombination of different components as further described with referenceto the example device shown in FIG. 8 .

In this example, the WLAN device 900 includes a low-power microprocessor902 and a high-power microprocessor 904 (e.g., microcontrollers ordigital signal processors) that process executable instructions. Thedevice also includes an input-output (I/O) logic control 906 (e.g., toinclude electronic circuitry). The microprocessors can includecomponents of an integrated circuit, programmable logic device, a logicdevice formed using one or more semiconductors, and otherimplementations in silicon and/or hardware, such as a processor andmemory system implemented as a system-on-chip (SoC). Alternatively or inaddition, the device can be implemented with any one or combination ofsoftware, hardware, firmware, or fixed logic circuitry that may beimplemented with processing and control circuits. The low-powermicroprocessor 902 and the high-power microprocessor 904 can alsosupport one or more different device functionalities of the device. Forexample, the high-power microprocessor 904 may execute computationallyintensive operations, whereas the low-power microprocessor 902 maymanage less complex processes such as detecting a hazard or temperaturefrom one or more sensors 908. The low-power processor 902 may also wakeor initialize the high-power processor 904 for computationally intensiveprocesses.

The one or more sensors 908 can be implemented to detect variousproperties such as acceleration, temperature, humidity, water, suppliedpower, proximity, external motion, device motion, sound signals,ultrasound signals, light signals, fire, smoke, carbon monoxide,global-positioning-satellite (GPS) signals, radio frequency (RF), otherelectromagnetic signals or fields, or the like. As such, the sensors 908may include any one or a combination of temperature sensors, humiditysensors, hazard-related sensors, other environmental sensors,accelerometers, microphones, optical sensors up to and including cameras(e.g., charged coupled-device or video cameras, active or passiveradiation sensors, GPS receivers, and radio frequency identificationdetectors. In implementations, the WLAN device 900 may include one ormore primary sensors, as well as one or more secondary sensors, such asprimary sensors that sense data central to the core operation of thedevice (e.g., sensing a temperature in a thermostat or sensing smoke ina smoke detector), while the secondary sensors may sense other types ofdata (e.g., motion, light or sound), which can be used forenergy-efficiency objectives or smart-operation objectives.

The WLAN device 900 includes a memory device controller 910 and a memorydevice 912, such as any type of a nonvolatile memory and/or othersuitable electronic data storage device. The WLAN device 900 can alsoinclude various firmware and/or software, such as an operating system914 that is maintained as computer executable instructions by the memoryand executed by a microprocessor. The device software may also include amotion sensing manager application 916 that implements aspects of motiondetection using wireless local area networks. The WLAN device 900 alsoincludes a device interface 918 to interface with another device orperipheral component, and includes an integrated data bus 920 thatcouples the various components of the WLAN device for data communicationbetween the components. The data bus in the WLAN device may also beimplemented as any one or a combination of different bus structuresand/or bus architectures.

The device interface 918 may receive input from a user and/or provideinformation to the user (e.g., as a user interface), and a receivedinput can be used to determine a setting. The device interface 918 mayalso include mechanical or virtual components that respond to a userinput. For example, the user can mechanically move a sliding orrotatable component, or the motion along a touchpad may be detected, andsuch motions may correspond to a setting adjustment of the device.Physical and virtual movable user-interface components can allow theuser to set a setting along a portion of an apparent continuum. Thedevice interface 918 may also receive inputs from any number ofperipherals, such as buttons, a keypad, a switch, a microphone, and animager (e.g., a camera device).

The WLAN device 900 can include network interfaces 922, such as a WLANinterface for communication with other WLAN devices in a WLAN network,and an external network interface for network communication, such as viathe Internet. The WLAN device 900 also includes wireless radio systems924 for wireless communication with other WLAN devices via the WLANinterface and for multiple, different wireless communications systems.The wireless radio systems 924 may include IEEE 802.11, Wi-Fi, ZigBee,Z-Wave, Thread, Bluetooth™, Mobile Broadband, BLE, and/or IEEE 802.15.4.Each of the different radio systems can include a radio device, antenna,and chipset that is implemented for a particular wireless communicationstechnology. The WLAN device 900 also includes a power source 926, suchas a battery and/or to connect the device to line voltage. An AC powersource may also be used to charge the battery of the device.

FIG. 10 illustrates an example system 1000 that includes an exampledevice 1002, which can be implemented as any of the WLAN devices thatimplement aspects of motion detection using wireless local area networksas described with reference to the previous FIGS. 1-9 . The exampledevice 1002 may be any type of computing device, client device, mobilephone, tablet, communication, entertainment, gaming, media playback,and/or other type of device. Further, the example device 1002 may beimplemented as any other type of WLAN device that is configured forcommunication on a WLAN, such as a thermostat, hazard detector, camera,light unit, commissioning device, router, border router, joiner router,joining device, end device, leader, access point, and/or other WLANdevices.

The device 1002 includes communication devices 1004 that enable wiredand/or wireless communication of device data 1006, such as data that iscommunicated between the devices in a WLAN, data that is being received,data scheduled for broadcast, data packets of the data, data that issynched between the devices, etc. The device data can include any typeof communication data, as well as audio, video, and/or image data thatis generated by applications executing on the device. The communicationdevices 1004 can also include transceivers for cellular phonecommunication and/or for network data communication.

The device 1002 also includes input/output (I/O) interfaces 1008, suchas data network interfaces that provide connection and/or communicationlinks between the device, data networks (e.g., a mesh network, externalnetwork, etc.), and other devices. The I/O interfaces can be used tocouple the device to any type of components, peripherals, and/oraccessory devices. The I/O interfaces also include data input ports viawhich any type of data, media content, and/or inputs can be received,such as user inputs to the device, as well as any type of communicationdata, as well as audio, video, and/or image data received from anycontent and/or data source.

The device 1002 includes a processing system 1010 that may beimplemented at least partially in hardware, such as with any type ofmicroprocessors, controllers, and the like that process executableinstructions. The processing system can include components of anintegrated circuit, programmable logic device, a logic device formedusing one or more semiconductors, and other implementations in siliconand/or hardware, such as a processor and memory system implemented as asystem-on-chip (SoC). Alternatively or in addition, the device can beimplemented with any one or combination of software, hardware, firmware,or fixed logic circuitry that may be implemented with processing andcontrol circuits. The device 1002 may further include any type of asystem bus or other data and command transfer system that couples thevarious components within the device. A system bus can include any oneor combination of different bus structures and architectures, as well ascontrol and data lines.

The device 1002 also includes computer-readable storage memory 1012,such as data storage devices that can be accessed by a computing device,and that provide persistent storage of data and executable instructions(e.g., software applications, modules, programs, functions, and thelike). The computer-readable storage memory described herein excludespropagating signals. Examples of computer-readable storage memoryinclude volatile memory and non-volatile memory, fixed and removablemedia devices, and any suitable memory device or electronic data storagethat maintains data for computing device access. The computer-readablestorage memory can include various implementations of random accessmemory (RAM), read-only memory (ROM), flash memory, and other types ofstorage memory in various memory device configurations.

The computer-readable storage memory 1012 provides storage of the devicedata 1006 and various device applications 1014, such as an operatingsystem that is maintained as a software application with thecomputer-readable storage memory and executed by the processing system1010. The device applications may also include a device manager, such asany form of a control application, software application, signalprocessing and control module, code that is native to a particulardevice, a hardware abstraction layer for a particular device, and so on.In this example, the device applications also include a motion sensingmanager application 1016 that implements aspects of motion detectionusing wireless local area networks, such as when the example device 1002is implemented as any of the WLAN devices described herein.

The device 1002 also includes an audio and/or video system 1018 thatgenerates audio data for an audio device 1020 and/or generates displaydata for a display device 1022. The audio device and/or the displaydevice include any devices that process, display, and/or otherwiserender audio, video, display, and/or image data, such as the imagecontent of a digital photo. In implementations, the audio device and/orthe display device are integrated components of the example device 1002.Alternatively, the audio device and/or the display device are external,peripheral components to the example device. In aspects, at least partof the techniques described for motion detection using wireless localarea networks may be implemented in a distributed system, such as over a“cloud” 1024 in a platform 1026. The cloud 1024 includes and/or isrepresentative of the platform 1026 for services 1028 and/or resources1030.

The platform 1026 abstracts underlying functionality of hardware, suchas server devices (e.g., included in the services 1028) and/or softwareresources (e.g., included as the resources 1030), and connects theexample device 1002 with other devices, servers, etc. The resources 1030may also include applications and/or data that can be utilized whilecomputer processing is executed on servers that are remote from theexample device 1002. Additionally, the services 1028 and/or theresources 1030 may facilitate subscriber network services, such as overthe Internet, a cellular network, or Wi-Fi network. The platform 1026may also serve to abstract and scale resources to service a demand forthe resources 1030 that are implemented via the platform, such as in aninterconnected device aspect with functionality distributed throughoutthe system 1000. For example, the functionality may be implemented inpart at the example device 1002 as well as via the platform 1026 thatabstracts the functionality of the cloud 1024.

Although aspects of motion detection using wireless local area networkshave been described in language specific to features and/or methods, thesubject of the appended claims is not necessarily limited to thespecific features or methods described. Rather, the specific featuresand methods are disclosed as example implementations of motion detectionusing wireless local area networks, and other equivalent features andmethods are intended to be within the scope of the appended claims.Further, various different aspects are described, and it is to beappreciated that each described aspect can be implemented independentlyor in connection with one or more other described aspects.

What is claimed is:
 1. A method for classifying motion, by a firstwireless local area network (WLAN) device, the method comprising:transmitting, by the first WLAN device, a sounding packet to a secondWLAN device, the first WLAN device being a WLAN Access Point (AP) andthe second WLAN device being a WLAN AP; receiving, from the second WLANdevice, an acknowledgement (ACK) of receiving the sounding packet by thesecond WLAN device; using the received ACK, determining Channel StateInformation (CSI) for a radio frequency (RF) signal path between thefirst WLAN device and the second WLAN device; aggregating the determinedCSI with additional CSI; and determining, using the aggregated CSI, apresence or a motion within a structure for classification.
 2. Themethod of claim 1, further comprising: receiving at least some of theadditional CSI from a third WLAN device.
 3. The method of claim 2,wherein the first WLAN device is a mesh WLAN Access Point (AP), whereinthe first WLAN device is a root AP in a mesh WLAN, wherein the secondWLAN device and the third WLAN device are APs in the mesh WLAN, themethod further comprising: scheduling, by the first WLAN device,transmission of sounding packets by the first, second, and third WLANdevices; and receiving, by the first WLAN device, at least some of theadditional CSI from the second and third WLAN devices.
 4. The method ofclaim 3, wherein the sounding packets are transmitted using a backhaulradio link between the APs in the mesh WLAN.
 5. The method of claim 2,wherein the third WLAN device is a WLAN Station (STA) in the WLAN, themethod further comprising: scheduling, by the first WLAN device,transmission of sounding packets by the first, second, and third WLANdevices; and receiving, by the first WLAN device, at least some of theadditional CSI from the second and third WLAN devices.
 6. The method ofclaim 1, further comprising: forwarding the determined presence ormotion to a cloud-based service that is effective to cause thecloud-based service to change state information related to thestructure.
 7. The method of claim 1, wherein the determining thepresence or the motion comprises: extracting motion features from theaggregated CSI; selecting motion features from the extracted motionfeatures; and classifying the selected motion features to determine thepresence or the motion.
 8. The method of claim 7, wherein the determinedpresence or motion is one of: no presence; a human presence; an animalpresence; a human sitting; a human walking; or a human falling.
 9. Themethod of claim 1, wherein the WLAN is an IEEE 802.11 WLAN, wherein thesounding packet is an IEEE 802.11 Null Data Frame (NDF), and wherein theACK is an IEEE 802.11 ACK.
 10. A wireless local area network (WLAN)device, configured as a WLAN Access Point (AP), comprising: a WLAN radiotransceiver; a processor and memory system to implement a motion sensingmanager application configured to: transmit, using the WLAN radiotransceiver, a sounding packet to another WLAN device, the other WLANdevice being a WLAN AP; receive, from the other WLAN device, anacknowledgement (ACK) of reception of the sounding packet by the otherWLAN device; using the received ACK, determine Channel State Information(CSI) for a radio frequency (RF) signal path between the WLAN device andthe other WLAN device; aggregate the determined CSI with additional CSI;and determine, using the aggregated CSI, a presence or a motion within astructure for classification.
 11. The WLAN device of claim 10, whereinthe motion sensing manager application is configured to determine thepresence or the motion within the structure by: extracting motionfeatures from the aggregated CSI; selecting motion features from theextracted motion features; and classifying the selected motion featuresto determine the presence or the motion.
 12. The WLAN device of claim10, wherein a third WLAN device is an Access Point (AP), and wherein theWLAN device is configured to receive at least some of the additional CSIfrom the third WLAN device.
 13. A method for classifying motion, by afirst wireless local area network (WLAN) device in a WLAN network, themethod comprising: determining, by the first WLAN device, that a secondWLAN device can be updated to transmit sounding packets for motionclassification; initiating an update request that is effective to causea cloud-based server to update software in the second WLAN device;receiving, by the first WLAN device, a first sounding packet from thesecond WLAN device after the update of the software of the second WLANdevice; and transmitting, to the second WLAN device, a firstacknowledgement (ACK) of receiving the first sounding packet, thetransmitting being effective to cause the second WLAN device to:determine Channel State Information (CSI) for a radio frequency (RF)signal path between the first WLAN device and the second WLAN device;and transmit the determined CSI to the first WLAN device.
 14. The methodof claim 13, the method further comprising: transmitting, by the firstWLAN device, a second sounding packet to the second WLAN device;receiving, from the second WLAN device, a second ACK of the secondsounding packet being received by the second WLAN device; using thereceived second ACK, determining Channel State Information (CSI) for aradio frequency (RF) signal path between the first WLAN device and thesecond WLAN device; receiving additional CSI from the second WLANdevice; aggregating the determined CSI with the additional CSI; anddetermining, using the aggregated CSI, a presence or a motion within astructure.
 15. The method of claim 13, wherein determining that thesecond WLAN device can be updated to transmit sounding packets formotion classification comprises: sending, by the first WLAN device, arequest to a cloud-based service to identify WLAN devices installed at astructure that can be updated to support motion classification.
 16. Themethod of claim 13, wherein initiating the update request is effectiveto cause the cloud-based server to send a notification to a user toapprove the update.
 17. The method of claim 13, further comprising:transmitting additional sounding packets to a third WLAN device that isnot capable of supporting motion classification; receiving, from thethird WLAN device, ACKs for the additional sounding packets received bythe third WLAN device; and using the ACKs for the additional soundingpackets, determining CSI for an RF signal path between the first WLANdevice and the third WLAN device.
 18. The method of claim 17, whereinthe first WLAN device is an IEEE 802.11 station (STA) device, whereinthe second WLAN device is an IEEE 802.11 STA device, and wherein thethird WLAN device is an IEEE 802.11 Access Point (AP).
 19. The method ofclaim 13, further comprising: scheduling, by the first WLAN device,transmissions of sounding packets by the second WLAN device; andtransmitting the schedule for sounding packet transmissions to thesecond WLAN device, the transmitting causing the second WLAN device totransmit sounding packets according to the schedule.
 20. The method ofclaim 13, wherein the WLAN is an IEEE 802.11 WLAN, wherein the soundingpacket is an IEEE 802.11 Null Data Frame (NDF), and wherein the ACK isan IEEE 802.11 ACK.